Recently, our team has achieved significant progress in the field of processing EEG motor imagery signals using parallel convolutional neural networks (CNNs).
Our team, in collaboration with Associate Professor Li Long’s team from the School of Artificial Intelligence, Shanghai University, has recently published a paper titled “A classification method for EEG motor imagery signals based on parallel convolutional neural network” in the international journal “Biomedical Signal Processing and Control” (IF: 2.954, Q2 in JCR and Chinese Academy of Sciences SCI journal ranking). The School of Computer Science at Shanghai University is listed as the first affiliation, with Associate Professor Han Yuexing as the first author and corresponding author.
Deep learning has been widely successful in various image classification tasks. However, its application to EEG (Electroencephalogram) motor imagery signal classification has been limited. In this study, we propose a preprocessing algorithm for representing EEG signals, followed by a parallel convolutional neural network (PCNN) architecture for classifying motor imagery signals. For the representation of raw EEG signals, a novel form of image is created to combine spatial filtering and frequency band extraction. By inputting the represented images into the PCNN, it stacks three unique sub-models together, aiming to optimize the classification performance. The proposed method achieves an average accuracy of 83.0±3.4% on the BCI Competition IV Dataset 2b, surpassing the compared methods by at least 5.2%. The average Kappa value of this method on Dataset 2b reaches 0.659±0.067, exhibiting at least a 20.5% improvement compared to the compared algorithms. These results demonstrate the effectiveness of this method in classifying motor imagery signals in EEG-based brain-computer interface (BCI) applications.